{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:08:08Z","timestamp":1753884488172,"version":"3.41.2"},"reference-count":40,"publisher":"World Scientific Pub Co Pte Ltd","issue":"15","funder":[{"DOI":"10.13039\/501100001809","name":"the Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62272170"],"award-info":[{"award-number":["62272170"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"the Fundamental Research Funds for the Central Universities under Grant","award":["YBNLTS2023-011"],"award-info":[{"award-number":["YBNLTS2023-011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:p> Although Deep Reinforcement Learning (DRL) is promising in solving Job Shop Scheduling Problems (JSPs), existing DRL-based methods still have large optimality gaps when learning job-to-machine solutions. This is mainly because: (i) existing state representations autonomously learned from graph-structured data cannot fully capture node information to support agents in making optimal decisions; and (ii) existing reward functions cannot accurately reflect some actions that will seriously worsen the current state. Aiming to address these issues, we propose a knowledge-based DRL method that selects nine well-known priority dispatching rules (PDRs) as state features, which can achieve effective model training. To avoid feature over-redundancy, we discard significantly correlated features based on the Pearson correlation relationship analysis, which can help to identify the key factors that affect the agents\u2019 decision-making. Furthermore, since it is difficult to design a reward function that can accurately distinguish actions, we mask poor-performing actions based on problem-specific knowledge to prevent them from being selected at the current decision point. Comprehensive experimental results demonstrate the superiority of our approach over four PDRs and four state-of-the-art methods on various benchmarks. <\/jats:p>","DOI":"10.1142\/s0218126624502608","type":"journal-article","created":{"date-parts":[[2024,3,28]],"date-time":"2024-03-28T15:03:20Z","timestamp":1711638200000},"source":"Crossref","is-referenced-by-count":0,"title":["Knowledge-Based Effective Dispatch for Job Shop Scheduling"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3924-5107","authenticated-orcid":false,"given":"Jiepin","family":"Ding","sequence":"first","affiliation":[{"name":"MoE Engineering Research Center of Software\/Hardware, Co-Design Technology and Application, East China Normal University, Shanghai 200062, P. R. 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